Positron emission tomography system and image reconstruction method using the same

ABSTRACT

Disclosed are a positron emission tomography system and an image reconstructing method using the same and the positron emission tomography system includes: a collection unit collecting a positron emission tomography sinogram (PET sinogram); an image generation unit applying the positron emission tomography sinogram to an MLAA with TOF and generating a first emission image and a first attenuation image and a NAC image reconstructed without attenuation correction; and a control unit selecting at least one of the first emission image, the first attenuation image and the NAC image generated by the image generation unit as an input image and generating and providing a final attenuation image by applying the input image to the learned deep learning algorithm.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation in part application of U.S. patentapplication Ser. No. 16/282,513, which was filed on Feb. 22, 2019, whichclaims priority to and the benefit of Korean Patent Application No.10-2018-0021902 filed in the Korean Intellectual Property Office on Feb.23, 2018, the entire contents of which are incorporated herein byreference.

BACKGROUND OF THE INVENTION (a) Field of the Invention

The present invention relates to a positron emission tomography systemand an image reconstruction method using the same.

(b) Description of the Related Art

Positron emission tomography (PET) is a nuclear medicine testing methodthat can show physiological/chemical and functional images of a body inthree dimensions using radiopharmaceuticals emitting positron.

At present, the positron emission tomography (CT) is widely used todiagnose various cancers and is evaluated as a useful test fordifferential diagnosis of cancer, stage setting, recurrence evaluation,and judgment of treatment effect.

In general, a positron emission tomography (PET) device needs to performa process of correcting and reconstructing an image in order to check atwhich portion of a body the radiopharmaceuticals are collected from rawdata and how much the radiopharmaceuticals are collected by providingthe raw data by detecting two disappearing radiations (gamma rays)emitted from the positron.

In recent years, a positron emission tomography image is reconstructedby using anatomical information obtained from additional scan ofcomputed tomography (CT) that obtains images by using an X-ray ormagnetic resonance imaging (MRI) that obtains images by transferringhigh-frequency waves in a magnetic field.

FIG. 1A is an exemplary diagram illustrating a process of reconstructinga positron emission tomography image in the related art.

As illustrated in FIG. 1A, in a process of reconstructing the positronemission tomography image to an attenuated corrected emission image(λ-CT) through a reconstruction algorithm (ordered subset expectationmaximization, OSEM), an attenuation image (μ-CT) obtained throughcomputed tomography of an X-ray is used.

Thus, reconstructing the image using the attenuation image obtained bythe additional scan can correct the image more accurately and imagequality is excellent, but an amount of a radiation received by a patientthrough the additional scan increases.

Therefore, in order to reconstruct the image with the positron emissiontomography only, a simultaneous reconstruction algorithm (MaximumLikelihood reconstruction of Attenuation and Activity withTime-Of-Flight, MLAA with TOF) which can simultaneously obtain anattenuation image (μ-MLAA) and an emission image (λ-MLAA) has beenstudied.

FIG. 1B is an exemplary diagram illustrating a process of generating anattenuation image and an emission image by using the MLAA with TOF inthe related art in a positron emission tomography image.

As illustrated in FIG. 1B, the attenuation image (μ-MLAA) and theemission image (λ-MLAA) can be obtained at the same time by using theMLAA with TOF without additional scan, but has a large error in theentire image from the attenuation image (μ-CT) obtained through theadditional scan in the related art.

In particular, since crosstalk artifact occurs in which a region whereactivity is high in the emission image appears in the attenuation imageand distortion thus occurs, there are many difficulties in using theattenuation image and the emission image obtained by using the MLAA withTOF.

Therefore, there is a need for a technique capable of simultaneouslyobtaining a high-quality attenuation image and a high-quality emissionimage by the positron emission tomography only without shot of separatemagnetic resonance imaging (MRI) or computed tomography (CT) forobtaining the attenuation image.

The above information disclosed in this Background section is only forenhancement of understanding of the background of the invention andtherefore it may contain information that does not form the prior artthat is already known in this country to a person of ordinary skill inthe art.

SUMMARY OF THE INVENTION

The present invention has been made in an effort to obtain ahigh-quality attenuation image and a high-quality emission image only bya positron emission tomography image by using a deep learning algorithm.

Exemplary embodiments according to the present invention can be used toachieve other objects not specifically mentioned other than the objects.

An exemplary embodiment of the present invention provides a positronemission tomography system including: a collection unit collecting apositron emission tomography sinogram (PET sinogram); a generation unitapplying an MLAA with TOF to the positron emission tomography sinogramand generating a first emission image and a first attenuation image, anda NAC image reconstructed without attenuation correction; and a controlunit selecting at least one of the first emission image, the firstattenuation image and the NAC image generated by the generation unit asan input image and generating and providing a final attenuation image byapplying the learned deep learning algorithm to the input image.

The positron emission tomography system may further include a learningunit collecting an attenuation image obtained through additionalscanning based on the positron emission tomography sinogram and learninga deep learning algorithm by using at least one of the first emissionimage, the first attenuation image and the NAC image generated from thepositron emission tomography sinogram and the obtained attenuationimage.

The learning unit may include an image generation unit generating asecond attenuation image from the first attenuation image through thedeep learning algorithm, an error calculation unit calculating an errorbetween the second attenuation image and the obtained attenuation image,and a weight adjustment unit performing repeated learning by readjustingweights of a plurality of filters included in the deep learningalgorithm so as to generate a final attenuation image in which the errorvalue becomes a value equal to or less than a threshold value.

The image generation unit may generate a plurality of feature maps fromthe input image by using the filter of the deep learning algorithm andgenerate a sample downsized from the generated feature map at apredetermined ratio, and repeat a process of generating the plurality offeature maps in the downsized sample by using the filter until a size ofthe sample reaches a predetermined reference size, and upsize the sampleat a predetermined ratio when the size of the sample reaches thepredetermined reference size and generate the second attenuation imagewhen the size of the upsized sample coincides with an initial size byrepeating a process of generating the plurality of feature maps in theupsized sample by using the filter.

The image generation unit may upsize the sample and collect feature mapshaving the same size as that of the upsized sample and combine thecollected feature maps and the sample at the time of generating theplurality of feature maps and generate the plurality of feature mapsfrom the combined sample.

The control unit may select the first attenuation image generated by theimage generation unit as an input image or select the first attenuationimage, the first emission image and the NAC image as the input image togenerate a final attenuation image through the learned deep learningalgorithm.

The control unit may select some voxel data groups from the firstattenuation image, the first emission image and the NAC image and applythe learned deep learning algorithm to the entire first attenuationimage, first emission image and the NAC image by repeating a process ofapplying the learned deep learning algorithm to the selected voxel datagroups to generate the final attenuation image in a 3D type.

The control unit may generate and provide a final emission imageobtained by correcting the final attenuation image by using the firstemission image and the final attenuation image.

Another exemplary embodiment of the present invention provides a methodfor reconstructing an image in a positron emission tomography system,including: collecting a positron emission tomography sinogram (PETsinogram); applying an MLAA with TOF to the positron emission tomographysinogram and generating a first emission image and a first attenuationimage, and a NAC image reconstructed without attenuation correction;selecting the generated first attenuation image as an input image;generating and providing a final attenuation image, the firstattenuation image and NAC image by applying the learned deep learningalgorithm to the input image; and generating and providing a finalemission image corrected by using the first emission image and the finalattenuation image.

Yet another exemplary embodiment of the present invention provides amethod for reconstructing an image in a positron emission tomographysystem, including: collecting a first emission image and a firstattenuation image generated by an MLAA with TOF with a positron emissiontomography sinogram (PET sinogram), and a NAC image reconstructedwithout attenuation correction; selecting at least one of the generatedfirst attenuation image the first attenuation image and NAC image as aninput image; generating and providing a final attenuation image byapplying the learned deep learning algorithm to the input image; andgenerating and providing a final emission image corrected by using thefirst emission image and the final attenuation image.

According to an exemplary embodiment of the present invention, it ispossible to obtain an attenuation image only by a positron emissiontomography image without additional CT or MRI imaging, therebyminimizing a radiation amount received by a patient.

According to an exemplary embodiment of the present invention, ahigh-quality attenuation image can be obtained through a simultaneousreconstruction technique, thereby reducing cost and time required forreconstructing a positron emission tomography image.

According to an exemplary embodiment of the present invention, it ispossible to provide a high-quality attenuation image by solving aquantitative error due to noise and crosstalk artifacts of an image,which occur due to the simultaneous reconstruction technique.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is an exemplary diagram illustrating a process of reconstructinga positron emission tomography image in the related art.

FIG. 1B is an exemplary diagram illustrating a process of generating anattenuation image and an emission image by using an MLAA with TOF in therelated art in the positron emission tomography image.

FIG. 2 is an exemplary diagram illustrating a communication networkincluding a positron emission tomography system according to anexemplary embodiment of the present invention.

FIG. 3 is a configuration diagram illustrating the positron emissiontomography system according to an exemplary embodiment of the presentinvention.

FIG. 4 is a configuration diagram of a learning unit according to anexemplary embodiment of the present invention.

FIG. 5 is a flowchart illustrating a process of learning a deep learningalgorithm according to an exemplary embodiment of the present invention.

FIGS. 6A, 6B, and 6C are exemplary diagrams for describing an inputimage of the deep learning algorithm according to an exemplaryembodiment of the present invention.

FIG. 7 is an exemplary diagram for describing the deep learningalgorithm according to an exemplary embodiment of the present invention.

FIG. 8 is an exemplary diagram for comparing a result learned through aU-net deep learning algorithm according to an exemplary embodiment ofthe present invention.

FIGS. 9A and 9B are exemplary diagrams for comparing images generatedthrough a plurality of deep learning algorithms according to anexemplary embodiment of the present invention.

FIG. 10 is a flowchart illustrating a method for reconstructing an imagein a positron emission tomography system according to an exemplaryembodiment of the present invention.

FIG. 11 is an exemplary diagram illustrating a deep learning algorithmof learning a 3D patch by an input image and an output image accordingto an exemplary embodiment of the present invention.

FIG. 12 is an exemplary diagram illustrating the attenuation imagereconstructed by using the 3D patch through the deep learning algorithmaccording to an exemplary embodiment of the present invention.

FIG. 13 is an exemplary diagram illustrating an SUV error measured withrespect to an emission image reconstructed by using the 3D patchaccording to an exemplary embodiment of the present invention.

FIG. 14 is an exemplary diagram of comparing ROI based SUV with respectto a reconstructed image and a CT based image according to an exemplaryembodiment of the present invention.

FIGS. 15A and 15B are exemplary diagrams for describing input image ofthe deep learning algorithm according to the other exemplary embodimentof the present invention.

FIGS. 16 and 17 are exemplary diagrams of comparing images generatedthrough input image of deep learning algorithms according to anexemplary embodiment of the present invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

An exemplary embodiment of the present invention will be described morefully hereinafter with reference to the accompanying drawings so as tobe easily implemented by those skilled in the art. As those skilled inthe art would realize, the described embodiments may be modified invarious different ways, all without departing from the spirit or scopeof the present invention. Parts not associated with description areomitted for clearly describing the present invention and like referencenumerals designate like elements throughout the specification. Further,a detailed description of the related art which is widely known will beomitted.

Throughout the specification, unless explicitly described to thecontrary, the word “comprise” and variations such as “comprises” or“comprising”, will be understood to imply the inclusion of statedelements but not the exclusion of any other elements.

FIG. 2 is an exemplary diagram illustrating a communication networkincluding a positron emission tomography system according to anexemplary embodiment of the present invention.

As illustrated in FIG. 2, a positron emission tomography device 100, apositron emission tomography system 200, and a user terminal 300 areconnected to each other through a network to transmit/receive data.

Here, the network may include all types of communication networks thattransfer data, which include a wired communication network, a local orremote wireless communication network, a network in which the wiredcommunication network and the local or remote wireless communicationnetwork are mixed, and the like.

First, the positron emission tomography device 100 as a positrontomography (PET) scanner may detect two annihilation radiations whichare formed in a cylindrical shape and simultaneously emitted.

In this case, the positron emission tomography device 100 is configuredsolely, not a type combined with a computed tomography (CT) or magneticresonance imaging (MRI).

The positron emission tomography system 200 then connects directly tothe positron emission tomography device 100 or collects a positronemission tomography (PET) sinogram through a storage device (notillustrated).

The positron emission tomography system 200 generates a first emissionimage and a first attenuation image in the positron emission tomographysinogram collected through an MLAA with TOF. In addition, the positronemission tomography system 200 applies the first attenuation image to alearned deep running algorithm to provide a final attenuation image.

The learned deep learning algorithm represents an artificial neuralnetwork that generates a final attenuation image having an error valueof a threshold value or less from an attenuation image generated fromanother imaging device.

In this case, the positron emission tomography system 200 may constructthe learned deep learning algorithm by repeatedly learning the deeplearning algorithm by using the first attenuation image and theattenuation image generated from another imaging device in order togenerate the final attenuation image.

Meanwhile, in FIG. 2, the positron emission tomography device 100 andthe positron emission tomography system 200 are illustrated as separatedevices, but the positron emission tomography system 200 may be latermounted on the positron emission tomography device 100 by a manager.

Next, the user terminal 300 refers to a medical device manager or aterminal of a medical staff who analyzes a positron emission tomographyimage and for example, the user terminal 300 is a personal computer, ahandheld computer, a personal digital assistant, a cellular phone, asmart device, a tablet, or the like.

The user terminal 300 may be linked with the positron emissiontomography 100 or the positron emission tomography system 200 to displayor store or manage the positron emission tomography sinogram and thefinal attenuation image.

Hereinafter, a positron emission tomography system 200 forreconstructing the positron emission tomography image through the deeplearning algorithm learned will be described in detail with reference toFIGS. 3 and 4.

FIG. 3 is a configuration diagram illustrating the positron emissiontomography system according to an exemplary embodiment of the presentinvention and FIG. 4 is a configuration diagram of a learning unitaccording to an exemplary embodiment of the present invention.

As illustrated in FIG. 3, the positron emission tomography system 200includes a collecting unit 210, a generating unit 220, a control unit230, and a learning unit 240.

First, the collection unit 210 collects the positron emission tomographysinogram. The positron emission tomography sinogram represents raw datagenerated from the positron emission tomography device 100.

Next, the generation unit 220 generates the first emission image and thefirst attenuation image by applying the MLAA with TOF to the positronemission tomography sinogram.

Here, the MLAA with TOF may include MLAA (A. Rezaei, et al. (2012),Simultaneous reconstruction of activity and attenuation intime-of-flight PET, Trans Med Imaging), MLAA-GMM (A. Mehranina and H.,Zaidi (2015), Joint Estimation of Activity and Attenuation in Whole-BodyTOF PET/MRI Using Constrained Gaussian Mixture Models, IEEE TRANSACTIONSON MEDICAL IMAGING), SMLGA-MLAA (S. C. Cade, et al., (2013), Use ofmeasured scatter data for the attenuation correction of single photonemission tomography without transmission scanning, Med Phys), MLACF (M.Defrise, et al. (2014) Transmission-less attenuation correction intime-of-flight PET: analysis of a discrete iterative algorithm, Phys MedBiol), but is not limited thereto.

The control unit 230 selects an image generated by the generation unit220 as an input image and applies the selected image to the learned deeplearning algorithm to generate and provide the final attenuation image.

The control unit 230 may select one or more of the first emission imageand the first attenuation image as the input image. For example, thecontrol unit 230 may select only the first attenuation image or selectthe first emission image and the first attenuation image.

Further, the control unit 230 may select a voxel data group among thefirst attenuation images as the input image or select the voxel datagroup in each of the first emission image and the first attenuationimage. Here, the voxel data group may include all of a 2D slice, a 3Dpatch, and a 3D image as a predetermined matrix size, but is not limitedthereto.

In addition, the learning unit 240 constructs the learned deep learningalgorithm by repeatedly making the deep learning algorithm forgenerating the final attenuation image be learned.

The learning unit 240 collects the attenuation image obtained throughadditional scan for the same person based on the positron emissiontomography sinogram and makes the deep learning algorithm be learned byusing the input image and the obtained attenuation image.

Here, the obtained attenuation image represents an attenuation imageobtained by photographing through a medical device different from thepositron emission tomography device 100. For example, the obtainedattenuation image may include the attenuation images obtained throughthe medical device such as the computed tomography (CT) or the magneticresonance imaging (MRI).

As illustrated in FIG. 4, the learning unit 240 includes an imagegeneration unit 241, an error calculation unit 242, and a weightadjustment unit 243.

First, the image generation unit 241 generates a second attenuationimage from the first attenuation image through the deep learningalgorithm.

Here, the deep learning algorithm is a convolutional neural network(CNN) or a generative adversarial network (GAN) (I. Goodfellow, et al.2014, Generative Adversarial Network, NIPS) which is known to bespecialized to image processing, but is not limited thereto.

Examples of the convolutional neural network (CNN) may include VGGNet(K., Simonyan, A., Zisserman, (2015), Very Deep Convolutional Networksfor Large-Scale Image Recognition, ICLR), ResNet (K., He, et al.,(2016), Deep Residual Learning for Image Recognition, CVPR), DnCNN (K.,Zhang, et al., (2016), Beyond a Gaussian Denoiser: Residual Learning ofDeep CNN for Image Denoising, Trans Image Processing), DenseNet (G.,Huang, et al., (2017), Densely Connected Convolutional Networks, CVPR),and the like.

In addition, the generative adversarial network (GAN) as a machinerunning for generating an image similar to original data distribution isused as a technique which may easily and rapidly make a real fake. Thegenerative adversarial network (GAN) learns through competition betweentwo neural network models of a generator G and a discriminator D andgenerates results. The generator G learns real data for the purpose ofgenerating data close to reality and generates data based on the learnedactual data and the discriminator D learns to discriminate whether thedata generated by the generator G is true or false.

Next, the error calculation unit 242 calculates an error value betweenthe second attenuation image and the obtained attenuation image.

The error calculation unit 242 may calculate the error value by using aDice coefficient, a percent error, a Bias and root-mean square error(RMSE), a cost function, etc., but the present invention is not limitedthereto.

Then, the weight adjustment unit 243 re-adjusts weights of a pluralityof filters included in the deep learning algorithm to repeatedly makethe deep learning algorithm be learned. In addition, the weightadjustment unit 243 may control to terminate learning when the errorvalue calculated by the error calculation unit 242 becomes a value equalto or less than a threshold value.

As described above, the learning unit 240 may construct the deeplearning algorithm learned so that the first attenuation image generatedin the positron emission tomography sinogram matches the obtainedattenuation image.

Meanwhile, the positron emission tomography system 200 may be a server,a terminal, or a combination thereof.

The terminal collectively refers to a device having a memory and aprocessor and having an arithmetic processing capability. For example,the terminal is a personal computer, a handheld computer, a personaldigital assistant (PDA), a cellular phone, a smart device, a tablet, andthe like.

The server may include a memory storing a plurality of modules, aprocessor that is connected to the memory, reacts to the plurality ofmodules, and processes service information provided to the terminal oraction information to control the service information, a communicationmeans, and a user interface (UI) display means.

The memory as a device storing information may include various types ofmemories including a high-speed random access memory, a magnetic diskstorage device, a flash memory device, a non-volatile memory including anon-volatile solid-state memory device, and the like.

The communication means transmits/receives the service information oraction information to/from the terminal in real time.

The UI display means outputs the service information or actioninformation of the device in real time. The UI display means may be anindependent device that directly or indirectly outputs or displays a UIor may be a part of the device.

Hereinafter, a method and a result of learning the deep learningalgorithm of the positron emission tomography system 200 will bedescribed in detail with reference to FIGS. 5 to 9B.

FIG. 5 is a flowchart illustrating a process of learning a deep learningalgorithm according to an exemplary embodiment of the present inventionand FIGS. 6A, 6B, and 6C are exemplary diagrams for describing an inputimage of the deep learning algorithm according to an exemplaryembodiment of the present invention.

FIG. 7 is an exemplary diagram for describing the deep learningalgorithm according to an exemplary embodiment of the present inventionand FIG. 8 is an exemplary diagram for comparing a result learnedthrough a deep learning algorithm according to an exemplary embodimentof the present invention.

As illustrated in FIG. 5, the positron emission tomography system 200collects an attenuation image obtained through additional scan tocorrespond to a positron emission tomography sinogram (S510).

The positron emission tomography system 200 collects attenuation imagesobtained by shots of a positron emission tomography sinogram and a CTmedical device for the same person.

In this case, the positron emission tomography system 200 may collectshot images by interlocking with each medical device in real time andcollect an image by accessing a separate database (not illustrated).

Next, the positron emission tomography system 200 applies the image toan MLAA with TOFTOF to generate a first emission image and a firstattenuation image (S520). In addition, the positron emission tomographysystem 200 selects an input image to be applied to the deep learningalgorithm (S530).

For example, the positron emission tomography system 200 may select onlythe first attenuation image as the input image or select the firstemission image and the first attenuation image as the input image.

FIG. 6A illustrates a process in which the positron emission tomographysystem 200 selects a first attenuation image (μ-MLAA) as an input imageand applies the selected first attenuation image to a deep learningalgorithm (CNN) to generate a final attenuation image (μ-CNN). Inaddition, FIG. 6B illustrates a process in which the positron emissiontomography system 200 selects a first emission image (λ-MLAA) and thefirst attenuation image (μ-MLAA) as the input image and applies theselected first emission image and first attenuation image to the deeplearning algorithm (CNN) to generate the final attenuation image(μ-CNN).

Meanwhile, as shown in FIG. 6C, the positron emission tomography system200 may select a two-dimensional slice type as the input image andgenerate the final attenuation image with a two-dimensional slicethrough the deep learning algorithm.

As described above, the positron emission tomography system 200 mayselect the first attenuation or the first emission image, and the firstattenuation image as a plurality of voxel data groups in the form of athree-dimensional patch.

In other words, the positron emission tomography system 200 may select atype of image to be input into a learning algorithm and select a formsuch as the 2D slice, the 3D patch, or an entire 3D image for theselected image.

Next, the positron emission tomography system 200 applies the selectedinput image to the deep learning algorithm to generate the secondattenuation image (S540).

As illustrated in FIG. 7, the positron emission tomography system 200applies the input image to the deep learning algorithm to make the deeplearning algorithm be learned from left to right.

FIG. 7 illustrates a process of learning through the U-net deep learningalgorithm among various deep learning algorithms including CAE, U-net,Hybrid, and the like, in which a first attenuation image and a firstemission image (activity) are selected as the input image in a 2D slicetype.

In addition, in FIG. 7, each box shape shows a multi-channel functionmap and the number and the size associated with each multi-channelfunction map as one example may be easily designed and changed by amanager later.

First, the positron emission tomography system 200 generates a pluralityof feature maps from the input image using a filter of the deep learningalgorithm.

As illustrated in FIG. 7, the positron emission tomography system 200generates several feature maps through a line using the filter in sizes(200*200) of the first attenuation image and the first emission image(activity). Here, the number of feature maps is two and each of thefeature maps is illustrated as 20 sheets, but is not limited thereto andmay be changed and designed by the manager later.

Next, the positron emission tomography system 200 generates a downsizedsample from the feature map at a predetermined ratio (Max pooling 20,100*100). Such a process is called pooling, and Max pooling using amaximum value is used in FIG. 7, but the present invention is notlimited thereto. In addition, the positron emission tomography system200 generates several feature maps through the line using the filter inthe downsized sample (100*100).

Again, the positron emission tomography system 200 generates a downsizedsample by using a plurality of feature maps at a predetermined ratio(Max pooling 40, 50*50). In addition, the positron emission tomographysystem 200 generates several feature maps through the line using thefilter in the downsized sample (50*50).

In other words, the positron emission tomography system 200 repeatedlygenerates the feature map and performs downsizing until the size of thedownsized sample reaches a predetermined reference size.

In addition, when a size (Max pooling 80, 25*25) of the downsized samplereaches a predetermined reference size, the positron emission tomographysystem 200 uses the plurality of feature maps and upsizes thecorresponding feature maps at a predetermined ratio (Deconv 80, 50*50)and collects the downsized feature maps (50*50) having a size which isthe same as that of the upsized sample.

The positron emission tomography system 200 collects samples (Copy &Concat) before downsizing and combines the collected samples with theupsized sample in order to compensate for a tendency of smoothing thefeature map generated through the filter.

As illustrated in 73 of FIG. 7, the feature maps of the same size arecollected and combined from the step of upsizing the sample and thefeature maps are generated from the combined samples. In addition, theplurality of feature maps are used and upsized at a predetermined ratio(Deconv 40, 100*100) and the feature maps having the same size arecollected and combined (72) and the feature maps are generated from thecombined samples.

The positron emission tomography system 200 generates the feature map byrepeating the process until the size of the feature map becomes equal tothe size of a first input image. In addition, the positron emissiontomography system 200 may generate the second attenuation image whichhas the same size as the first input image.

Meanwhile, the positron emission tomography system 200 may not collectthe feature map of the same size when the size of the upsized sample isthe same as the initial image input size, such as a hybrid deep learningalgorithm. In other words, it is possible to exclude the step (71 inFIG. 7) of collecting an initially generated feature map according to apossibility that the initially generated feature map will include a lotof noise and combining the collected feature map with the upsizedsample.

Next, the positron emission tomography system 200 calculates an errorbetween the second attenuation image and the obtained attenuation image(S550).

The positron emission tomography system 200 may calculate the errorvalue by using a Dice coefficient, a percent error, a Bias and root-meansquare error (RMSE), a cost function, etc.

For example, the positron emission tomography system 200 may calculatethe Dice coefficient between the second attenuation image generatedthrough the deep learning algorithm and the obtained attenuated imagethrough Equation 1 and compare similarities between the two images.

In other words, the similarity may be compared using the Dicecoefficient that measures an overlap of bones and air regions in eachimage.

$\begin{matrix}{{D_{type} = \frac{2 \times {n\left( {\mu_{type}^{PET}\bigcap\mu_{type}^{CT}} \right)}}{{n\left( \mu_{type}^{PET} \right)} + {n\left( \mu_{type}^{CT} \right)}}}\;} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack \\{\; {{type} = \left\{ \begin{matrix}{bone} \\{air}\end{matrix} \right.}} & \;\end{matrix}$

A value of the Dice coefficient has values from 0 to 1 and as the valueof the Dice coefficient is closer to 0, the similarity is low and as thevalue of the Dice coefficient is closer to 1, the similarity is high.

In this case, when the positron emission tomography system 200calculates the similarity by using the Dice coefficient, the positronemission tomography system 200 may estimate the error based on thecalculated similarity.

Meanwhile, the positron emission tomography system 200 may calculate theerror by setting a region of interest in the image and calculating thepercent error in the second attenuation image and the obtainedattenuation image.

Besides, the positron emission tomography system 200 may include variousmethods capable of measuring the error or similarity between the secondattenuation image and the obtained attenuation image.

In addition, the positron emission tomography system 200 compares thecalculated error value and a predetermined threshold value (S560).

In this case, the positron emission tomography system 200 readjusts theweight of the filter included in the deep learning algorithm when theerror value is larger than the predetermined threshold (S770).

Meanwhile, in a process of initially learning the deep learningalgorithm, weights applied to a plurality of filters are randomlyinitialized, and the respective weights are readjusted so that thesecond attenuation image generated through learning approaches theobtained attenuation image.

The positron emission tomography system 200 then returns to step S540with the deep learning algorithm that includes the readjusted weight ofthe filter. As described above, the positron emission tomography system200 may enhance a matching degree between the second attenuation imagegenerated while repeating steps S540 to S570 and the obtainedattenuation image (μ-CT).

Meanwhile, when the error value is smaller than the predeterminedthreshold value in step S560, the positron emission tomography system200 generates the learned deep learning algorithm to which the weight ofthe filter is applied (S580). As described above, the positron emissiontomography system 200 may construct the learned deep learning algorithmand store and manage the learned algorithm.

In this case, positron emission tomography system 200 may make aplurality of deep learning algorithms be learned and select andconstruct a deep learning algorithm having a smallest error from theobtained attenuation image (Ground Truth, μ-CT) which becomes acriterion.

FIG. 8 is an exemplary diagram illustrating an input image (Inputs,λ-MLAA, μ-MLAA) and the final attenuation image (μ-Unet) of the deeplearning algorithm and the obtained attenuation image (Ground Truth,μ-CT).

As illustrated in FIG. 8, it can be seen that in a coronal, a sagittal,and a transaxial of the brain, in the case of the first emission image(λ-MLAA) and the first attenuation image (μ-MLAA) generated by the MLAAwith TOF, the images themselves have a lot of noise and a portion (redsystem color) in which the activity is highly exhibited in the firstemission image (λ-MLAA) appears in the first attenuation image (μ-MLAA).

In detail, a plurality of arrows illustrated in FIG. 8 as regions thatare not well estimated by the MLAA algorithm and show a large differencefrom the CT indicate a greatly improved region in the final attenuationimage (μ-Unet) generated by the deep learning algorithm.

First, a yellow arrow indicates a crosstalk artifact in which a regionin which the activity is highly exhibited in the emission image appearsin the attenuation image and is distorted. When μ-MLAA and μ-CT arecompared with each other, it can be seen that a portion indicated by ayellow arrow is estimated to be low in μ-MLAA and displayed to berelatively dark. On the contrary, in μ-Unet, it can be seen that such aphenomenon is improved.

Next, an orange arrow indicates a skull region and in the μ-MLAA, it canbe seen that there is a tendency in which an anterior part of the skullis estimated to be thicker than the μ-CT and a posterior part isestimated to be thinner than the μ-CT. On the contrary, it can be seenthat the thickness in the μ-Unet is improved to a thickness almostsimilar to the thickness in the μ-CT. As described above, an imagequality of the final attenuation image (μ-Unet) is more distinct thanthe image quality of the first attenuation image (μ-MLAA) through theU-net deep learning algorithm and it can be seen that a quantitativeerror is minimized due to the noise and artifact described above.Moreover, it can be seen that the final attenuation image (μ-Unet) isvery similar to the attenuation image (μ-CT) obtained through the CTshot. Table 1 below shows a result of calculating a similarity betweenthe final attenuation image generated through the deep learningalgorithm by using the Dice coefficient and the obtained attenuationimage by the positron emission tomography system 200.

TABLE 1 Whole Head Cranial Region Method D_(bone) D_(air) D_(bone)D_(air) MLAA 0.374 ± 0.058 0.317 ± 0.070 0.399 ± 0.063 0.426 ± 0.062 CNN0.717 ± 0.047 0.513 ± 0.057 0.747 ± 0.047 0.523 ± 0.063 (CAE) CNN 0.787± 0.042 0.575 ± 0.047 0.801 ± 0.043 0.580 ± 0.053 (U-net) CNN 0.794 ±0.037 0.718 ± 0.048 0.810 ± 0.038 0.738 ± 0.044 (Hybrid)

Table

Referring to Table 1, the first attenuation image and the obtainedattenuation image are compared with each other based on the attenuationimages measured in a whole head and a cranial region and it can be seenthat the Dice coefficient is generally larger in the attenuation imageshowing the cranial region than in the attenuation image showing thewhole head.

In addition, it can be seen that the similarity of the final attenuationimage generated in CNN (Hybrid), CNN (U-net), or CNN (CAE) with theobtained attenuation image is higher than the similarity of the firstattenuation image generated in MLAA.

Hereinafter, the first attenuation image generated through the MLAA withTOF, final attenuation images generated by using various deep learningalgorithms, and the attenuation image obtained through the CT arecompared with each other by using FIG. 9.

FIGS. 9A and 9B are exemplary diagrams for comparing images generatedthrough a plurality of deep learning algorithms according to anexemplary embodiment of the present invention.

First, as illustrated in (A) of FIG. 9A, referring to a graph shown bygenerating an RMSE error value for the image of Sagittal, it can be seenthat an image having a largest error is the first attenuation image(μ-MLAA) at a rightmost side and subsequent images are CAE, U-net, andHybrid.

In particular, when the attenuation images of MLAA and Hybrid arecompared with each other, it can be seen that the error which appears inthe attenuation image MLAA is reduced to 50% or less in the attenuationimage generated by Hybrid.

In (B) of FIG. 9A, {circle around (1)}, {circle around (2)}, and {circlearound (3)} as results corresponding to respective locations of {circlearound (1)}, {circle around (2)}, and {circle around (3)} in (A) of FIG.9A show the first attenuation image (μ-MLAA) for the transaxial of thebrain and the attenuation images obtained by CAE, U-net, Hybrid, and theCT image as the attenuation image. Referring to (B) of FIG. 9A, it canbe seen that the second attenuation image generated by the Hybrid deeplearning algorithm is most similar to the obtained attenuation image(CT).

(A) of FIG. 9B is a graph showing the percent error calculated withrespect to the first attenuation image (μ-MLAA) and the attenuationimage (CAE, U-net, and Hybrid) through the deep learning algorithm. Inaddition, (B) of FIG. 9B is a graph of measuring a specific bindingratio with respect to the first attenuation image (μ-MLAA) and theattenuation image (CAE, U-net, and Hybrid) through the deep learningalgorithm and showing the calculated percent error.

(A) of FIG. 9B illustrates a value obtained by setting the region ofinterest with respect to four regions, i.e., cerebellum, occipitalcortex, caudate head, and putamen, which is a subcortical region andcalculating the percent error from the attenuation image (CT) obtainedwith respect to each region.

In (A) of FIG. 9B, a long vertical box indicates a standard deviation ineach calculated percent error value and a horizontal bar in the longvertical box indicates a median value.

In the comparison based on the median value in (A) of FIG. 9B, it can beseen that the attenuation image of MLAA and the attenuation image by CAEhave the largest error and a percent error value between the attenuationimages through the U-net and Hybrid deep learning algorithms is small.

Next, (B) of FIG. 9B illustrates a value obtained by measuring thespecific binding ratio with respect to a region which is the same as theregion of interest selected in (A) of FIG. 9B and calculating thepercent error from the attenuation image (CT) obtained for each region.

In (B) of FIG. 9B, the long vertical box indicates a standard deviationin each calculated percent error value and the horizontal bar in thelong vertical box indicates the median value.

In the comparison based on the median value in (B) of FIG. 9B, it can beseen that the first attenuation image of MLAA shows an error of 10% ormore, while all of the attenuation images (CAE, U-net, and Hybrid)through the deep learning algorithm have a very small error ofapproximately 5%.

As described above, it can be seen that the final attenuation imagegenerated through the deep learning algorithm proposed by the presentinvention has a very high similarity with the obtained attenuation image(CT) and the noise and the error are greatly reduced as compared withthe first attenuation image of the MLAA.

Hereinafter, a process of generating a final attenuation image and afinal emission image using the positron emission tomography sinogramcollected in real time using the learned deep learning algorithm will bedescribed in detail.

FIG. 10 is a flowchart illustrating a method for reconstructing an imagein a positron emission tomography system according to an exemplaryembodiment of the present invention.

The positron emission tomography system 200 collects the positrontomography sinogram (S1010). In addition, the positron emissiontomography system 200 applies the image to the MLAA with TOF to generatea first emission image and a first attenuation image (S1020).

In this case, the positron emission tomography system 200 may collectthe first emission image and the first attenuation image generated bythe MLAA with TOF in the positron tomography sinogram without collectingthe positron tomography sinogram.

In other words, in the positron emission tomography system 200, a stepof collecting the first emission image and the first attenuation imagegenerated by the MLAA with TOF in an external device or a database whichis interlocked may be replaced without going through steps S1010 andS1020.

Next, the positron emission tomography system 200 selects an input imageto be applied to the deep learning algorithm (S1030).

The positron emission tomography system 200 may select a type of imageinput into a learning algorithm and select a type such as the 2D slice,the 3D patch, or the entire 3D image for the selected image.

Hereinafter, the positron emission tomography system 200 will bedescribed by assuming each of a case (CNN1) of selecting the voxel datagroup from the first attenuation image and a case (CNN2) of selectingthe voxel data group from the first emission image and the firstattenuation image.

Next, the positron emission tomography system 200 applies the inputimage to the learned deep learning algorithm to generate and provide thefinal attenuation image (S1040).

The positron emission tomography system 200 may select a correspondinglearned deep learning algorithm based on the type and the form of theselected input image.

FIG. 11 is an exemplary diagram illustrating a deep learning algorithmof learning a 3D patch by an input image and an output image accordingto an exemplary embodiment of the present invention.

As illustrated in FIG. 11, the positron emission tomography system 200may select the voxel data group in each of the first emission image andthe first attenuation image generated through the MLAA with TOF.

Here, the deep learning algorithm may be divided into the case (CNN1)where the 3D patch is selected from the first attenuation image as theinput image of the deep learning algorithm and the case (CNN2) where the3D patch is selected from each of the first emission image and the firstattenuation image.

Further, all inputs and labels may be used in a matrix size of 32*32*32,which is not limited to the shape of the cube in one example, and may beeasily changed and designed later.

Similarly to the deep learning algorithm learning method describedthrough FIG. 5, the positron emission tomography system 200 calculatesthe feature map in the input image and adjusts the weight of the filterof the deep learning algorithm so as to approach the attenuation imageobtained from the CT image while adjusting the size.

The positron emission tomography system 200 may calculate the errorvalue by using the cost function between the 3D patch type secondattenuation image calculated through the deep learning algorithm and theattenuation image obtained from the CT image and repeats learning whilereadjusting the weight of the filter of the deep learning algorithm soas to minimize the cost function.

As described above, the positron emission tomography system 200 maygenerate the final attenuation image of the 3D patch type through the 3Dpatch type input image by using the learned deep learning algorithm.

In this case, the positron emission tomography system 200 derives thefinal attenuation image in the type of the 3D image regardless ofwhether the type of the input is the 2D slice or the 3D patch. Forexample, when the input type is the 2D slice, the positron emissiontomography system 200 may generate the 3D image as the final attenuationimage by combing a plurality of 2D slices. FIG. 12 is an exemplarydiagram illustrating the attenuation image reconstructed by using the 3Dpatch through the deep learning algorithm according to an exemplaryembodiment of the present invention.

As illustrated in FIG. 12, it can be seen that in the first emissionimage (MLAA act(λ)) and the first attenuation image (MLAA atn(μ))generated through the MLAA with TOF, the noise and the crosstalkartifact are significantly exhibited.

Further, it can be seen that when the case (CNN1) of selecting the 3Dpatch in the first attenuation image and the case (CNN2) of selectingthe 3D patch in each of the first emission image and the firstattenuation image are compared with the attenuation image (CT-based)obtained from the CT image, a higher similarity with the CT-based imagein CNN2 is provided than in CNN1.

In detail, it can be seen that the noise is significantly reduced andskeleton identification is enhanced in CNN2 as compared with CNN1.Therefore, it can be seen that a scheme (CNN2) applied to the deeplearning algorithm learned by using both the first emission image andthe first attenuation image has a better result in terms of anatomicaldetails.

Meanwhile, the Dice coefficient of the attenuation image obtained fromMLAA, CNN1, CNN2, and the CT image is calculated through Equation 2below as shown in Table 2 below.

$\begin{matrix}{{D_{type} = \frac{2 \times {n\left( {\mu_{type}^{method}\bigcap\mu_{type}^{CT}} \right)}}{{n\left( \mu_{type}^{method} \right)} + {n\left( \mu_{type}^{CT} \right)}}}{{method} = \left\{ {{\begin{matrix}{MLAA} \\{{CNN}\; 1} \\{{CNN}\; 2}\end{matrix}{type}} = \left\{ \begin{matrix}{bone} \\{water} \\{fat} \\{lung}\end{matrix} \right.} \right.}} & \left\lbrack {{Equation}\mspace{14mu} 2} \right\rbrack\end{matrix}$

TABLE 2 n = 20 Method D_(bone) D_(water) D_(fat) D_(lung) MLAA 0.363 ±0.068 0.686 ± 0.030 0.587 ± 0.037 0.328 ± 0.156 CNN1 0.722 ± 0.061 0,854± 0.024 0.807 ± 0.046 0.857 ± 0.052 CNN2 0.771 ± 0.066 0.883 ± 0.0270.842 ± 0.056 0.863 ± 0.053

The closer the value of the Dice coefficient is to 1, the higher thesimilarity. Thus, it can be seen from Table 2 that the final attenuationimage generated by CNN2 has the highest similarity.

As described above, it can be seen that the final attenuation imagegenerated through the deep learning algorithm also has a very highsimilarity with the obtained attenuation image (CT) by using the 2Dslice and the 3D patch type and the noise and the error are greatlyreduced as compared with the first attenuation image of the MLAA.

Next, the positron emission tomography system 200 generates and providesthe final emission image corrected by using the first emission image andthe final attenuation image (S1050).

The positron emission tomography system 200 may reconstruct theattenuated and corrected final emission image by using a reconstructionalgorithm (OSEM).

FIG. 13 is an exemplary diagram illustrating an SUV error measured withrespect to an emission image reconstructed by using the 3D patchaccording to an exemplary embodiment of the present invention.

FIG. 13 is a diagram of measuring an SUV error in an emission imagereconstructed by using Equation 3 and comparing accuracy of attenuationcorrection by using MLAA and CNN results.

$\begin{matrix}{{{Absolute}\mspace{14mu} {difference}} = {\lambda_{*} - \lambda_{CT}}} & \left\lbrack {{Equation}\mspace{14mu} 3} \right\rbrack \\{{{Percent}\mspace{14mu} {difference}} = {\frac{\lambda_{*} - \lambda_{CT}}{\lambda_{CT}} \times 100}} & \;\end{matrix}$

Here, λ* represents one emission image among MLAA, CNN1, or CNN2.

As illustrated in FIG. 13, it can be seen that in both CNN1 and CNN2,the error may be remarkably reduced as compared with CT basedattenuation correction and an absolute difference and a percentdifference are the smallest in CNN2.

Next, FIG. 14 is an exemplary diagram of comparing ROI based SUV withrespect to a reconstructed image and a CT based image according to anexemplary embodiment of the present invention.

FIG. 14 illustrates a result of comparing SUV based on a region ofinterest (ROI) with bone lesions. Specifically, FIG. 14 as graphsshowing a correlation between each of MLAA, CNN1, or CNN2 image for theregion of interest (ROI) with the bone lesions and the CT based imageillustrated that the correlation is higher as being closer to areference line Y=X.

It can be seen that CNN1 and CNN2 have better consistency with respectto the reference line Y=X than MLAA. In particular, it can be seen thata value of the correlation of CNN2 is higher.

Hereinafter, as an image reconstruction method according to anotherembodiment of the present invention, a process of generating a finalattenuation image using NAC (nonattenuation-corrected PET) image, thefirst attenuation image and the first emission image.

Here, the NAC image refers to an NAC PET activity images reconstructedwithout attenuation correction of the sinogram.

FIGS. 15A and 15B are exemplary diagrams for describing input image ofthe deep learning algorithm according to the other exemplary embodimentof the present invention.

As illustrated in FIG. 15A the positron emission tomography system 200selects first emission image (λ-MLAA), the first attenuation image(μ-MLAA), and the NAC image as input images of a deep learning algorithm(CNN).

The positron emission tomography system 200 may acquire a NAC image (NACPET activity image, λ-NAC) through a subset expectation maximization(OSEM) algorithm arranged in a PET scan image.

In addition, the positron emission tomography system 200 generates afirst emission image and a first attenuation image in the positronemission tomography sinogram collected through an MLAA with TOFF.

In this case, in order to solve the non-unique a global scaling problemin the simultaneous reconstruction algorithm (MLAA), a boundaryconstraint may be applied during the attenuation map estimation in theMLAA.

As described above, the positron emission tomography system 200 appliesthe first attenuation image, the first emission image and NAC image to alearned deep running algorithm to provide a final attenuation image.

In addition, 15B shows a deep learning algorithm (CNN3, U-net) forderiving a final attenuation image (μ-CNN) by inputting a first emissionimage (λ-MLAA), a first attenuation image (μ-MLAA), and a NAC image(λ-NAC).

As illustrated in 15B, when each voxel data group is input from each ofthe three input images, iterative learning is performed in the samemanner as in the deep learning algorithm learning method describedabove.

Specifically, the positron emission tomography system 200 is trained bydesigning deep learning algorithms (CNN3, U-net) to predict theground-truth CT-derived μ-map (μ-CT) from λMLAA, μ-MLAA, and λ-NAC.

Here, the inputs to the CNN are the 32×32×32 matrix patches randomlyextracted from λ-MLAA, μ-MLAA, and λ-NAC, and labels are same sizedpatches from μ-CT at the corresponding location. Each convolution anddeconvolution layer is composed of a 3×3×3 kernel, except for the lastlayer in which a 1×1×1 circuit is used for scaling purposes, and theseconfigurations can be easily changed and designed later.

The positron emission tomography system 200 calculates the feature mapin the input image and adjusts the weight of the filter of the deeplearning algorithm so as to approach the attenuation image obtained fromthe CT image while adjusting the size. The positron emission tomographysystem 200 may calculate the error value by using the cost functionbetween the 3D patch type second attenuation image calculated throughthe deep learning algorithm and the attenuation image obtained from theCT image and repeats learning while readjusting the weight of the filterof the deep learning algorithm so as to minimize the cost function.

As described above, the positron emission tomography system 200 maygenerate the final attenuation image of the 3D patch type through the 3Dpatch type input image by using the learned deep learning algorithm.

FIGS. 16 and 17 are exemplary diagrams of comparing images generatedthrough input image of deep learning algorithms according to anexemplary embodiment of the present invention.

FIGS. 16 and 17 compare CT images and generated final attenuated imagesbased on the PET scan image taken from the head to the upper body.

FIGS. 16 and 17, (a) is a final attenuation image derived using only theNAC image as an input image μ-CNN(_(NAC)), and (b) is a finalattenuation derived from the first attenuation image and the firstemission image as an input image. The image μ-CNN(_(MLAA)) is shown. And(c) shows the final attenuation image μ-CNN(_(MLAA+NAC)) derived fromthe NAC image, the first attenuation image, and the first emission imageaccording to another embodiment of the present invention as inputimages, and (d) is Shows the μ-CT image.

μ-CNN(_(NA)c) images showed some blocky and discontinuity artifacts inlung and upper liver, whereas μ-CNN(_(MLAA)) images achieved better boneand air cavity delineation and showed better contrast at some fat andwater boundaries than μ-CNN_((NAC)) images.

Meanwhile, μ-CNN (_(MLAA+NAC)) shows most similar to CT images in thebone contours of the shoulder, ribs and spine, and it can be seen thatthe moisture and fat contrast are the best

As shown in Table 3 the similarity of the CNN-derived μ-maps derived bythe three methods relative to the μ-CT is measured using the peaksignal-to-noise ratio (PSNR), and structural similarity index (SSIM).

And the activity error relative to the λ-CT is calculated using thenormalized root mean square error (NRMSE).

TABLE 3 CNN_(NAC) CNN_(MLAA) CNN_(MLAA+ NAC) μ PSNR 26.51 30.00 30.61SSIM 0.752 0.790 0.794 λ NRMSE 0.0031 0.0026 0.0025

Referring to Table 3, comparing the similarity of the μ-map with theerror of the λ-map, it can be seen that the case of using only MLAA(CNN_(MLAA)) has less error than the case of using only NAC (CNN_(NAC)).And it can be seen that among the three methods, when MLAA and NAC areused together (CNN_(MLAA+NAC)), the error is the least.

As described above, according to the present invention, it is possibleto provide a high-quality attenuation image by solving a quantitativeerror due to noise and crosstalk artifacts of an image, which occur dueto the simultaneous reconstruction technique without an additionalscanning process of a medical device such as a separate CT or MRI.

A program for executing a method according to an exemplary embodiment ofthe present invention may be recorded in a computer readable recordingmedium.

The computer readable medium may include singly a program command, adata file, or a data structure or a combination thereof. The medium maybe specially designed and configured for the present invention, or maybe publicly known to and used by those skilled in the computer softwarefield. Examples of the computer-readable recording medium includemagnetic media such as a hard disk, a floppy disk, and a magnetic tape,optical media such as a CD-ROM and a DVD, magneto-optical media such asa floptical disk, and a hardware device which is specifically configuredto store and execute the program command such as a ROM, a RAM, and aflash memory. Here, the medium may be a transmission medium such as anoptical or metal line, a wave guide, or the like, including a carrierwave for transmitting a signal designating a program command, a datastructure, and the like. Examples of the program command include ahigh-level language code executable by a computer by using aninterpreter, and the like, as well as a machine language code created bya compiler.

While this invention has been described in connection with what ispresently considered to be practical example embodiments, it is to beunderstood that the invention is not limited to the disclosedembodiments, but, on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the spirit andscope of the appended claims.

DESCRIPTION OF SYMBOLS

-   -   100: Positron emission tomography device    -   200: Positron emission tomography system    -   210: Collection unit    -   220: Generation unit    -   230: Control unit    -   240: Learning unit    -   241: Image generation unit    -   242: Error calculation unit    -   243: Weight adjustment unit    -   300: User terminal

What is claimed is:
 1. A positron emission tomography system comprising:a collection unit collecting a positron emission tomography sinogram(PET sinogram); a generation unit applying an MLAA with TOF to thepositron emission tomography sinogram and generating a first emissionimage and a first attenuation image, and a NAC image reconstructedwithout attenuation correction; and a control unit selecting at leastone of the first emission image and the first attenuation image and theNAC image generated by the generation unit as an input image andgenerating and providing a final attenuation image by applying thelearned deep learning algorithm to the input image.
 2. The positronemission tomography system of claim 1, further comprising: a learningunit collecting an attenuation image obtained through additionalscanning based on the positron emission tomography sinogram and making adeep learning algorithm be learned by using at least one of the firstemission image, the first attenuation image and the NAC image generatedfrom the positron emission tomography sinogram and the obtainedattenuation image.
 3. The positron emission tomography system of claim2, wherein: the learning unit includes, an image generation unitgenerating a second attenuation image from the first attenuation imagethrough the deep learning algorithm, an error calculation unitcalculating an error between the second attenuation image and theobtained attenuation image, and a weight adjustment unit performingrepeated learning by readjusting weights of a plurality of filtersincluded in the deep learning algorithm so as to generate a finalattenuation image in which the error value becomes a value equal to orless than a threshold value.
 4. The positron emission tomography systemof claim 3, wherein: the image generation unit generates a plurality offeature maps from the input image by using the filter of the deeplearning algorithm and generates a sample downsized from the generatedfeature map at a predetermined ratio, and repeats a process ofgenerating the plurality of feature maps in the downsized sample byusing the filter until a size of the sample reaches a predeterminedreference size, and upsizes the sample at a predetermined ratio when thesize of the sample reaches the predetermined reference size andgenerates the second attenuation image when the size of the upsizedsample coincides with an initial size by repeating a process ofgenerating the plurality of feature maps in the upsized sample by usingthe filter.
 5. The positron emission tomography system of claim 4,wherein: the image generation unit upsizes the sample and collectsfeature maps having the same size as the upsized sample and combines thecollected feature maps and the sample at the time of generating theplurality of feature maps and generates the plurality of feature mapsfrom the combined sample.
 6. The positron emission tomography system ofclaim 1, wherein: the control unit, selects the first attenuation, thefirst emission image and the NAC image as the input image to generate afinal attenuation image through the learned deep learning algorithm. 7.The positron emission tomography system of claim 6, wherein: the controlunit, selects some voxel data groups from the first attenuation image,the first emission image and the NAC image and applies the learned deeplearning algorithm to the entire first attenuation image, first emissionimage and the NAC image by repeating a process of applying the learneddeep learning algorithm to the selected voxel data groups to generatethe final attenuation image in a 3D type.
 8. The positron emissiontomography system of claim 1, wherein: the control unit, generates andprovides a final emission image obtained by correcting the finalattenuation image by using the first emission image and the finalattenuation image.
 9. A method for reconstructing an image in a positronemission tomography system, the method comprising: collecting a positronemission tomography sinogram (PET sinogram); applying an MLAA with TOFto the positron emission tomography sinogram and generating a firstemission image and a first attenuation image, and a NAC imagereconstructed without attenuation correction; selecting the generatedfirst attenuation image, the first attenuation image and NAC image as aninput image; generating and providing a final attenuation image byapplying the learned deep learning algorithm to the input image; andgenerating and providing a final emission image corrected by using thefirst emission image and the final attenuation image.
 10. A method forreconstructing an image in a positron emission tomography system, themethod comprising: collecting a first emission image and a firstattenuation image generated by an MLAA with TOF with a positron emissiontomography sinogram (PET sinogram), and a NAC image reconstructedwithout attenuation correction; selecting at least one of the generatedfirst attenuation image, the first attenuation image and NAC image as aninput image; generating and providing a final attenuation image byapplying the learned deep learning algorithm to the input image; andgenerating and providing a final emission image corrected by using thefirst emission image and the final attenuation image.
 11. The method ofclaim 9, further comprising: collecting an attenuation image obtainedthrough additional scanning, which corresponds to the positron emissiontomography image and learning a deep learning algorithm by using theinput image and the obtained attenuation image.
 12. The method of claim11, wherein: the learning of the deep learning algorithm includesgenerating a second attenuation image from the first attenuation imagethrough the deep learning algorithm, calculating an error between thesecond attenuation image and the obtained attenuation image, andperforming repeated learning by readjusting weights of a plurality offilters included in the deep learning algorithm so as to generate afinal attenuation image in which the error value becomes a value equalto or less than a threshold value.
 13. The method of claim 12, wherein:the generating of the second attenuation image includes generating aplurality of feature maps from the input image by using a filter of thedeep learning algorithm, generating a sample downsized from thegenerated feature map at a predetermined ratio and generating theplurality of feature maps by using the filter in the downsized sample,upsizing the sample at a predetermined ratio and collecting a downsizedfeature map having the same size as the upsized sample when the samplereaches a predetermined reference size, generating the plurality offeature maps by using the filter in the upsized sample and the collectedfeature map, and generating a second attenuation image when a size ofthe upsized sample coincides with an initial size.
 14. The method ofclaim 9, wherein: when some voxel data groups are selected in the firstattenuation image or the first emission image or NAC image as the inputimage, in the generating and providing of the final attenuation image,the final attenuation image is generated in a 3D type by applying thelearned deep learning algorithm to the selected voxel data groups. 15.The method of claim 10, further comprising: collecting an attenuationimage obtained through additional scanning, which corresponds to thepositron emission tomography image and learning a deep learningalgorithm by using the input image and the obtained attenuation image.16. The method of claim 15, wherein: the learning of the deep learningalgorithm includes generating a second attenuation image from the firstattenuation image through the deep learning algorithm, calculating anerror between the second attenuation image and the obtained attenuationimage, and performing repeated learning by readjusting weights of aplurality of filters included in the deep learning algorithm so as togenerate a final attenuation image in which the error value becomes avalue equal to or less than a threshold value.
 17. The method of claim16, wherein: the generating of the second attenuation image includesgenerating a plurality of feature maps from the input image by using afilter of the deep learning algorithm, generating a sample downsizedfrom the generated feature map at a predetermined ratio and generatingthe plurality of feature maps by using the filter in the downsizedsample, upsizing the sample at a predetermined ratio and collecting adownsized feature map having the same size as the upsized sample whenthe sample reaches a predetermined reference size, generating theplurality of feature maps by using the filter in the upsized sample andthe collected feature map, and generating a second attenuation imagewhen a size of the upsized sample coincides with an initial size. 18.The method of claim 10, wherein: when the first attenuation image, thefirst emission image and NAC image are selected as the input image, inthe generating and providing of the final attenuation image, the finalattenuation image is generated by applying the learned deep learningalgorithm to the first attenuation image and the first emission image.19. The method of claim 10, wherein: when some voxel data groups areselected in the first attenuation image or the first emission image orNAC image as the input image, in the generating and providing of thefinal attenuation image, the final attenuation image is generated in a3D type by applying the learned deep learning algorithm to the selectedvoxel data groups.